Apparatus, systems, and methods for analyzing movements of target entities

ABSTRACT

The present disclosure relates to apparatus, systems, and methods for providing a location information analytics mechanism. The location information analytics mechanism is configured to analyze location information to extract contextual information (e.g., profile) about a mobile device or a user of a mobile device, collectively referred to as a target entity. The location information analytics mechanism can include analyzing location data points associated with a target entity to determine features associated with the target entity, and using the features to predict attributes associated with the target entity. The set of predicted attributes can form a profile of the target entity.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/352,664, filed Mar. 13, 2019, which is a continuation of U.S.application Ser. No. 15/960,322, filed Apr. 23, 2018, which is acontinuation of U.S. application Ser. No. 15/420,655, filed Jan. 31,2017, now U.S. Pat. No. 9,977,792, which is a continuation of U.S.application Ser. No. 14/214,208, filed Mar. 14, 2014, now U.S. Pat. No.9,594,791, which claims benefit of the earlier filing date, under 35U.S.C. § 119(c), of:

-   -   U.S. Provisional Application No. 61/799,986, filed on Mar. 15,        2013, entitled “SYSTEM FOR ANALYZING AND USING LOCATION BASED        BEHAVIOR”;    -   U.S. Provisional Application No. 61/800,036, filed on Mar. 15,        2013, entitled “GEOGRAPHIC LOCATION DESCRIPTOR AND LINKER”;    -   U.S. Provisional Application No. 61/799,131, filed on Mar. 15,        2013, entitled “SYSTEM AND METHOD FOR CROWD SOURCING DOMAIN        SPECIFIC INTELLIGENCE”;    -   U.S. Provisional Application No. 61/799,846, filed Mar. 15,        2013, entitled “SYSTEM WITH BATCH AND REAL TIME DATA.        PROCESSING”; and    -   U.S. Provisional Application No. 61/799,817, filed on Mar. 15,        2013, entitled “SYSTEM FOR ASSIGNING SCORES TO LOCATION        ENTITIES.”

This application is also related to:

-   -   U.S. patent application Ser. No. 14/214,296, filed on Mar. 14,        2014, entitled “APPARATUS, SYSTEMS, AND METHODS FOR PROVIDING        LOCATION INFORMATION”;    -   U.S. patent application Ser. No. 14/214,213, filed on Mar. 14,        2014, entitled “APPARATUS, SYSTEMS, AND METHODS FOR CROWDSOURING        DOMAIN SPECIFIC INTELLIGENCE”;    -   U.S. patent application Ser. No. 15/132,228, filed on Apr. 18,        2016, entitled “APPARATUS, SYSTEMS, AND METHODS FOR BATCH AND        REALTIME DATA PROCESSING”;    -   U.S. patent application Ser. No. 14/214,309, filed on Mar. 14,        2014, entitled “APPARATUS, SYSTEMS, AND METHODS FOR ANALYZING        CHARACTERISTICS OF ENTITIES OF INTEREST”; and    -   U.S. patent application Ser. No. 14/214,231, filed on Mar. 14,        2014, entitled “APPARATUS, SYSTEMS, AND METHODS FOR GROUPING        DATA RECORDS.”

The entire content of each of the above-referenced applications(including both the provisional applications and the non-provisionalapplications) is herein incorporated by reference.

FIELD OF THE INVENTION

The present disclosure generally relates to data processing apparatus,systems, and methods for analyzing movements of target entities.

BACKGROUND

Many service providers have access to location information of manymobile devices. The service providers are in communication with mobiledevices that are equipped with a geo-location system, such as a GlobalPositioning System (GPS), configured to determine the location of theassociated mobile device, and these mobile devices can share theirlocation information with the service providers. The locationinformation can be useful for service providers because the serviceproviders can adapt the service based on the location of the mobiledevices. For example, mobile application that recommends restaurants canre-rank a list of recommended restaurants based on the determinedlocation of the mobile device running that application.

While the use of location information has increased significantly inrecent years, the use of location information is still quite limited.The location information is often deemed a time-independent measurement.Therefore, although service providers adapt their services based on thelocation of the mobile device at a particular time instance, the serviceproviders largely ignore the path over which the mobile device hastravelled over a period of time. Thus, the service providers largelyignore the rich contextual information embedded in the locationinformation. There is a need to provide efficient mechanisms forextracting rich contextual information embedded in the locationinformation.

SUMMARY

In general, in an aspect, embodiments of the disclosed subject mattercan include an apparatus. The apparatus includes one or more interfacesconfigured to provide communication with a computing device. Theapparatus also includes a processor in communication with the one ormore interfaces. The processor is configured to run one or more modulesthat are operable to cause the apparatus to receive, from the computingdevice, a time-series of location data points corresponding to a targetentity, determine one or more attributes associated with the targetentity based on the time-series of location data points, and provide aprofile of the target entity based on the one or more attributesassociated with the target entity.

In general, in an aspect, embodiments of the disclosed subject mattercan include a method. The method can include receiving, by a firstcomputing device from a second computing device, a time-series oflocation data points corresponding to a target entity, determining, bythe first computing device, one or more attributes associated with thetarget entity based on the time-series of location data pointscorresponding to the target entity, and providing, by the firstcomputing device, a profile of the target entity based on the one ormore attributes associated with the target entity.

In general, in an aspect, embodiments of the disclosed subject mattercan include a non-transitory computer readable medium. Thenon-transitory computer readable medium can include executableinstructions operable to cause a data processing apparatus to receive,from a computing device in communication with the data processingapparatus, a time-series of location data points corresponding to atarget entity, determine one or more attributes associated with thetarget entity based on the time-series of location data pointscorresponding to the target entity, and provide a profile of the targetentity based on the one or more attributes associated with the targetentity.

In any one of the embodiments disclosed herein, the apparatus, themethod, or the non-transitory computer readable medium can includemodules, steps, or executable instructions for determining an accuracyof the time-series of location data points, and discard, based on thedetermined accuracy, one or more of the location data points in thetime-series of location data points.

In any one of the embodiments disclosed herein, the apparatus, themethod, or the non-transitory computer readable medium can includemodules, steps, or executable instructions for determining the accuracyof the time-series of location data points based on a time-series oflocation data points associated with other target entities.

In any one of the embodiments disclosed herein, the apparatus, themethod, or the non-transitory computer readable medium can includemodules, steps, or executable instructions for determining the accuracyof the time-series of location data points at a particular time instancebased on location information associated with other target entities atthe particular time instance.

In any one of the embodiments disclosed herein, the apparatus, themethod, or the non-transitory computer readable medium can includemodules, steps, or executable instructions for determining one or moresessions from the time-series of location data points by grouping one ormore of the time-series of location data points that are bounded inspace and/or time, and determining the one or more attributes associatedwith the target entity based on the one or more sessions.

In any one of the embodiments disclosed herein, the apparatus, themethod, or the non-transitory computer readable medium can includemodules, steps, or executable instructions for determining one or moresessions from the time-series of location data points by grouping one ormore of the time-series of location data points that are bounded inspace and/or time, determining one or more clusters based on the one ormore sessions based on a physical proximity between sessions, anddetermining the one or more attributes associated with the target entitybased on the one or more sessions and the one or more clusters.

In any one of the embodiments disclosed herein, the apparatus, themethod, or the non-transitory computer readable medium can includemodules, steps, or executable instructions for associating one of thelocation data points, the sessions, or the clusters with annotationinformation associated with a geographical location of the location datapoints, sessions, or clusters, and using the annotation information todetermine the one or more attributes associated with the target entity.

In any one of the embodiments disclosed herein, the apparatus, themethod, or the non-transitory computer readable medium can includemodules, steps, or executable instructions for determining the one ormore attributes associated with the target entity based on movements ofthe target entity between two or more clusters.

In any one of the embodiments disclosed herein, the apparatus, themethod, or the non-transitory computer readable medium can includemodules, steps, or executable instructions for determining a homelocation attribute based on, at least in part, statistical measures onthe movements of the target entity and the annotation informationassociated with the target entity.

In any one of the embodiments disclosed herein, the apparatus, themethod, or the non-transitory computer readable medium can includemodules, steps, or executable instructions for determining a homelocation attribute based on, at least in part, a likelihood that aparticular location is associated with a residence.

In any one of the embodiments disclosed herein, the apparatus, themethod, or the non-transitory computer readable medium can includemodules, steps, or executable instructions for determining a homelocation attribute based on, at least in part, timestamps of locationdata points associated with the target entity.

In any one of the embodiments disclosed herein, the apparatus, themethod, or the non-transitory computer readable medium can includemodules, steps, or executable instructions for receiving the time-seriesof location data points in a batch mode, wherein the computing device isa server operated by a service provider.

In any one of the embodiments disclosed herein, the apparatus, themethod, or the non-transitory computer readable medium can includemodules, steps, or executable instructions for receiving the time-seriesof location data points in a streaming mode, wherein the computingdevice is the target entity.

In any one of the embodiments disclosed herein, the apparatus, themethod, or the non-transitory computer readable medium can includemodules, steps, or executable instructions for determining a predictivemodel based on the one or more attributes, wherein the predictive modelis configured to predict a behavior of the target entity in a future.

DESCRIPTION OF THE FIGURES

Various objects, features, and advantages of the present disclosure canbe more fully appreciated with reference to the following detaileddescription when considered in connection with the following drawings,in which like reference numerals identify like elements. The followingdrawings are for the purpose of illustration only and are not intendedto be limiting of the disclosed subject matter, the scope of which isset forth in the claims that follow.

FIG. 1 illustrates a diagram of a location information analytics systemin accordance with some embodiments.

FIG. 2 illustrates an area of activity (AoA) in accordance with someembodiments.

FIG. 3 illustrates a set of area of activities (AoAs) accordance withsome embodiments.

FIG. 4 illustrates patterns of movements between AoAs in accordance withsome embodiments.

FIGS. 5A-5C illustrate a profile of user activities around AoAs inaccordance with some embodiments.

FIG. 6 illustrates a process for generating a profile of a target entityin accordance with some embodiments.

FIG. 7 illustrates a process for clustering two or more sessions into acluster in accordance with some embodiments.

FIG. 8 illustrates an example of how location data points are groupedinto sessions and how sessions are grouped into clusters in accordancewith some embodiments.

FIGS. 9A-9G illustrate such a division for a small geographic area inaccordance with some embodiments.

FIG. 10 illustrates a geographic attribute of a profile of a targetentity in accordance with some embodiments.

FIG. 11 illustrates a home location attribute of a profile of a targetentity in accordance with some embodiments.

FIG. 12 illustrates a list of location entities provided in a profile ofa target entity in accordance with some embodiments.

FIGS. 13A-13B illustrate the DMA attribute and the Metro attribute of aprofile of a target entity in accordance with some embodiments.

FIG. 14 illustrates demographic attributes of a profile of a targetentity in accordance with some embodiments.

FIG. 15 illustrates behavioral attributes of a profile of a targetentity in accordance with some embodiments.

FIG. 16 illustrates a profile of a target entity in a tabular form inaccordance with some embodiments.

FIGS. 17A-17B illustrate a process for training and applying apredictive behavioral model in accordance with some embodiments.

DESCRIPTION OF THE DISCLOSED SUBJECT MATTER

The present disclosure relates to apparatus, systems, and methods forproviding a location information analytics mechanism. The locationinformation analytics mechanism is configured to analyze locationinformation to extract contextual information (e.g., profile) about amobile device or a user of a mobile device, collectively referred to asa target entity. At a high level, the location information analyticsmechanism can include analyzing location data points associated with atarget entity to determine features associated with the target entity,and using the features to predict attributes associated with the targetentity. The set of predicted attributes can form a profile of the targetentity.

More particularly, the location information analytics mechanism cananalyze a time-series of location information (also referred to aslocation data points, or simply as location information). Locationinformation can include a geospatial coordinate, an address, or anylocation identifier that is capable of identifying a location. Thelocation information analytics mechanism can analyze the time-series oflocation information, such as a time series of geospatial coordinatesproduced by a target entity, to determine characteristics (e.g.,features) associated with the target entity. Subsequently, the locationinformation analytics mechanism can use the determined features of thetarget entity to determine one or more profiles of the target entity. Aprofile can include a set of high level attributes that describes atarget entity, including categorizations based on the target entity'sbehavior. For example, the location information analytics mechanism cangenerate a profile, indicating that a particular mobile device is usedby a person that has a primary residence in Beverly Hills, Calif., andhas a primary work place in Los Angeles, Calif. The profile can be usedby service providers, such as publishers and developers that servecontent on a target entity, to personalize their applications andcustomize their content to the target entity.

In some embodiments, a feature of an entity used by the locationinformation analytics mechanism can include temporally independentinformation about an entity (or a user of an entity). For example, afeature can indicate that a user of a mobile device often visits aprimary school, that the user often visits a shopping mall, and that theuser's apartment is located near Los Angeles, Calif. The feature of anentity can also provide temporal relationships between temporallyindependent information. Referring to the example provided above, thefeature can indicate that the user of the mobile device visit the schoolattended by the user's child in the morning, that the user subsequentlyvisits a shopping mall, that the user returns to the school attended bythe user's child in the afternoon, and that the user subsequentlyreturns to the user's apartment near Los Angeles. The locationinformation analytics mechanism can use these features to predictinformation about the user, for example, that the user does not workfull-time during the day.

In some embodiments, the profile can be used by service providers topredict future behaviors of the target entity, and provide adaptedservices to the target entity based on the predicted future behaviors.For example, the location information analytics mechanism can use one ormore profiles to generate a predictive model that can predict thelocation and/or the type of locations/venues that a corresponding targetentity may be located at any given time. These profiles can also becombined to determine the characteristics of a group of target entitiesas a whole.

In some embodiments, the disclosed location information analyticsmechanism can efficiently determine the one or more profiles of a targetentity by representing a time-series of location information usingsessions and clusters. A time-series of location information includes asequence of location data points measured typically at successivetemporal points. In some cases, the successive temporal points can beuniformly spaced; in other cases, the successive temporal points can benon-uniformly spaced. The disclosed location information analyticsmechanism can segment the time-series of location information togenerate sessions and clusters. For example, the location informationanalytics mechanism can group (or segment) geospatial coordinates intosessions, and group the sessions into clusters. Such session andcluster-based representation of the time-series of location informationcan obviate the need to recompute or reprocess all the entiretime-series of location information when additional location informationfor new time instances are received.

In some embodiments, the disclosed location information analyticsmechanism can associate location data points, sessions, and/or clustersof a target entity with annotation information, which may providemetadata about the location data points, sessions, and/or clusters. Theannotation information can be one of the features used to determineattributes associated with the target entity. The annotation informationcan be received from external sources, such as a website, a database, orany source information to which the disclosed location informationanalytics mechanism has access.

In some embodiments, the disclosed location information analyticsmechanism can provide a profile based on as few as 1 location datapoint. However, the accuracy and contents of the generated profile canimprove as the number of location data points increases. Therefore, insome embodiments, the disclosed location information analytics mechanismcan provide a profile based on more than 3 location data points, 5location data points, 10 location data points, or any predeterminednumber of location data points.

In some embodiments, one or more attributes in the profile can beassociated with a confidence score, indicating a confidence score oraccuracy of the associated attributes. In some cases, the confidencescore can range between 0 and 1. However, any other ranges can be usedto represent the confidence score.

In some cases, where the disclosed location information analyticsmechanism has access to a large number of location data points over along period of time, the disclosed location information analyticsmechanism can determine a time-dependent (or time-bracketed) profile ofa target entity. For example, the disclosed location informationanalytics mechanism can determine that a user of a mobile device enjoyshaving late-night snacks at In-N-Out between 11 PM-1 AM around LosAngeles, Calif.

In some embodiments, the location information analytics mechanism canuse one or more machine learning techniques to determine or refineattributes in one or more profiles. For example, if a particularattribute of a profile of a target entity is missing, then the locationinformation analytics mechanism can fill in (or predict) the missinginformation based on known information about the target entity. In someembodiments this can be done using logistic regression. In otherembodiments it can use other machine learning techniques such as but notlimited to random forests, linear regression, hidden Markov models, andneural networks.

A machine learning technique, for the purpose of the locationinformation analytics mechanism, can include any function that receivesa collection of “training data” (for example, specified as rows, each ofwhich contains multiple feature scalars and one or more target scalar),and produces an estimator or “model” that predicts the target value frominput features for new rows of data not comprising the training data.The quality of the estimator can be measured as, for example, looselyspeaking, its ability to predict a target value for new rows of data.For example, given examples of “golfers” and a set of annotated sessionsfor known “golfers” (i.e., the training data), the system may determinethat “sessions on golf courses” strongly correlate to “golfers” andassign a value to the “sessions on golf courses” feature that results ina more optimal target value prediction for “golfer”.

In practice, as many as millions of such features can interact to createa model to predict such targets. In some embodiments, the system caninclude machine learning techniques that cluster or group targets orprofiles. In such cases, the system can include any function thatreceives a collection of data (for example specified as rows, each ofwhich contains multiple feature scalars) and produces an estimator or“model” that predicts a cluster of targets. For example, such functionscan help identify similar features (e.g. behavior patterns) that appearsto form a group. This grouping can be used to suggest new targetprofiles that can be given a subsequent name (e.g. “cluster 123 ofsimilar things” becomes “dive bar lovers” after a human interprets acommon qualitative aspect that the cluster members possess). Inaddition, the grouping can be correlated to desired behavior orqualities (e.g. clicking on ads) and other members of the cluster can beflagged as desirable for advertisers (without necessarily interpretingwhat qualitative aspects make them so) and targeted for ads.

In some embodiments, the location information analytics mechanism canuse a separate estimator for each profile (e.g., each behavioralsegment). For example, the location information analytics mechanism canbuild feature scalars from observable attributes such as “number ofvisits to a restaurant per week”, or “number of visits to Starbucks”,and use a separate estimator for different variables, such as “user is afood connoisseur,” or “user is especially affluent.”

The location information analytics mechanism can optionally include anintelligent data processing mechanism for cleaning (or discarding)inaccurate location data points. Location data points can beintermittent and of varying quality. The quality of location informationcan vary due to a large variance in its source and accuracy. Forexample, the location information can be determined based on a largenumber of sources: the Internet Protocol (IP)-address of a mobiledevice, the cell tower to which a mobile device is attached, the WiFiaccess point to which a mobile device is attached, and/or a GlobalPositioning System (GPS) operating in the mobile device. However, theaccuracy of the determined location information can vary significantly.The location information determined from the IP address of a mobiledevice is generally highly inaccurate, whereas the location informationdetermined from the WiFi access point or the GPS is generally moreaccurate. To address this issue, the disclosed location informationanalytics mechanism incorporates a number of data verificationtechniques that cleans the location information on submission. Dependingon the application, the location information analytics mechanism candiscard, optionally, about 15-25% of location information as irrelevantto profile building. This data cleaning process can improve the accuracyof generated profiles of target entities.

FIG. 1 illustrates a diagram of a location information analytics systemin accordance with some embodiments. The system 100 includes a server102, a communication network 104, one or more client devices 106, and aservice provider 118. The server 102 can include a processor 108, amemory device 110, a location information formatting (LIF) module 112, alocation information analytics (LIA) module 114, and one or moreinterfaces 116.

The processor 108 of the server 102 can be implemented in hardware. Theprocessor 108 can include an application specific integrated circuit(ASIC), programmable logic array (PLA), digital signal processor (DSP),field programmable gate array (FPGA), or any other integrated circuit.The processor 108 can also include one or more of any other applicableprocessors, such as a system-on-a-chip that combines one or more of aCPU, an application processor, and flash memory, or a reducedinstruction set computing (RISC) processor. The memory device 110 of theprocessor 108 can include a computer readable medium, flash memory, amagnetic disk drive, an optical drive, a programmable read-only memory(PROM), and/or a read-only memory (ROM).

The LIF module 112 can be configured to receive a time series oflocation information of a target entity, for example, a temporal seriesof geo-location coordinates corresponding to a target entity's movement,and segment the time series of location information into sessions.Furthermore, the LIF module 112 can also group (or merge) two or moresessions into a cluster, and, optionally, add annotation information tosessions and/or clusters based on information from external datasources. In some embodiments, the LIF module 112 can extract, fromgeo-location coordinates associated with multiple target entities, allgeo-location coordinates corresponding to a single target entity andgenerate sessions, clusters, and/or annotation information correspondingto the single target entity. In some embodiments, the annotationinformation can include one or more of: demographic information such ascensus data on a location housing cost statistics of a location (e.g.from public records to know average home sales price or rental cost);ambient noise measurements collected from devices previously at thelocation at various times; sentiment or keywords from social networkdata (e.g. tweets or social network-site posts) originating at thelocation; names and categories of nearby businesses; keywords andratings from reviews of nearby businesses; crime statistics; satelliteimagery (e.g. to determine if there's a pool); lot line polygons frompublic records (e.g. to determine size of a residence, such as a house,apartments, or a condo); accelerometer data collected from devices nearthe location (e.g. to know whether it's foot traffic or vehicle).

In some embodiments, the LIF module 112 can receive geo-locationcoordinates in a batch mode. In other embodiments, the LIF module 112can receive the geo-location coordinates in a streaming mode.

The L1A module 114 can be configured to analyze the sessions, clusters,and/or the annotation information generated by the LIF module 112 todetermine a profile of a target entity, such as a client 106. Theprofile can include a set of high level attributes that describes atarget entity. Depending on the associated high-level attributes, aprofile can be characterized as a behavioral profile, describingbehavioral characteristics of a target entity; a demographic profile,describing a demographic grouping or a market segment corresponding to atarget entity, such as age bands, social class hands, and gender bands;or a geographic profile, describing locations of a connected series ofevents or locations visited by a target entity. One or more of thebehavioral profile, the demographic profile, and the geographic profilecan form a single aggregate profile for a target entity.

In some embodiments, the LIA module 114 can use a machine learningtechnique to generate a profile. For example, the LIA module 114 can usea random forest technique to determine attributes of profiles from thesessions, dusters, and/or annotation information.

In some embodiments, the LIF module 112 and/or the LIA module 114 can beimplemented in software stored in the non-transitory memory device 110,such as a non-transitory computer readable medium. The software storedin the memory device 110 can run on the processor 108 capable ofexecuting computer instructions or computer code.

In some embodiments, the LIF module 112 and/or the LIA module 114 can beimplemented in hardware using an ASIC, PLA, DSP, FPGA, or any otherintegrated circuit. In some embodiments, the LIP module 112 and the LIAmodule 114 can both be implemented on the same integrated circuit, suchas ASIC, PLA, DSP, or FPGA, thereby forming a system on chip.

In some embodiments, the server 102 can receive the location informationof a target entity from a service provider 118. The service provider 118can communicate with one or more clients 106 to receive location datapoints associated with the clients 106, and provide the receivedlocation data points to the server 102. In some embodiments, the serviceprovider 118 can aggregate the location data points over a predeterminedperiod of time, and provide the aggregated location data points in bulk.In other embodiments, the service provider 118 can stream the locationdata points, or send location data points aggregated over a short periodof time, to the server 102. The service provider 118 can include asoftware service provider, an application provider, a communicationservice provider, a publisher, or any other types of service providers.

In some embodiments, the server 102 can communicate with clients 106directly, for example via a software application programming interface(API), to receive location information of the clients 106. The server102 can subsequently analyze the location information to computeprofiles of the clients 106. Then, the server 102 can provide thecomputed profiles to service providers incrementally or in a bulk mode,or when interesting new attributes are added to the profiles.

The server 102 can include one or more interfaces 116. The one or moreinterfaces 116 provide a communication mechanism to communicate internalto, and external to, the server 102. For example, the one or moreinterfaces 116 enable communication with clients 106 and/or the serviceprovider 118 over the communication network 104. The one or moreinterfaces 116 can also provide an application programming interface(API) to other servers, service providers 118, or computers coupled tothe network 104 so that the server 102 can receive location information,such as geo-location coordinates. The one or more interfaces 116 areimplemented in hardware to send and receive signals in a variety ofmediums, such as optical, copper, and wireless, and in a number ofdifferent protocols some of which may be non-transitory.

In some embodiments, the server 102 can operate using an operatingsystem (OS) software. In some embodiments, the OS software is based on aLinux software kernel and runs specific applications in the server suchas monitoring tasks and providing protocol stacks. The OS softwareallows server resources to be allocated separately for control and datapaths. For example, certain packet accelerator cards and packet servicescards are dedicated to performing routing or security control functions,while other packet accelerator cards/packet services cards are dedicatedto processing user session traffic. As network requirements change,hardware resources can be dynamically deployed to meet the requirementsin some embodiments.

The server's software can be divided into a series of tasks that performspecific functions. These tasks communicate with each other as needed toshare control and data information throughout the server 102. A task canbe a software process that performs a specific function related tosystem control or session processing. Three types of tasks operatewithin the server 102 in some embodiments: critical tasks, controllertasks, and manager tasks. The critical tasks control functions thatrelate to the server's ability to process calls such as serverinitialization, error detection, and recovery tasks. The controllertasks can mask the distributed nature of the software from the user andperform tasks such as monitoring the state of subordinate manager(s),providing for intra-manager communication within the same subsystem, andenabling inter-subsystem communication by communicating withcontroller(s) belonging to other subsystems. The manager tasks cancontrol system resources and maintain logical mappings between systemresources.

Individual tasks that run on processors in the application cards can bedivided into subsystems. A subsystem is a software element that eitherperforms a specific task or is a culmination of multiple other tasks. Asingle subsystem includes critical tasks, controller tasks, and managertasks. Some of the subsystems that run on the server 102 include asystem initiation task subsystem, a high availability task subsystem, ashared configuration task subsystem, and a resource managementsubsystem.

The system initiation task subsystem is responsible for starting a setof initial tasks at system startup and providing individual tasks asneeded. The high availability task subsystem works in conjunction withthe recovery control task subsystem to maintain the operational state ofthe server 102 by monitoring the various software and hardwarecomponents of the server 102. Recovery control task subsystem isresponsible for executing a recovery action for failures that occur inthe server 102 and receives recovery actions from the high availabilitytask subsystem. Processing tasks are distributed into multiple instancesrunning in parallel so if an unrecoverable software fault occurs, theentire processing capabilities for that task are not lost.

Shared configuration task subsystem can provide the server 102 with anability to set, retrieve, and receive notification of serverconfiguration parameter changes and is responsible for storingconfiguration data for the applications running within the server 102. Aresource management subsystem is responsible for assigning resources(e.g., processor and memory capabilities) to tasks and for monitoringthe task's use of the resources.

In some embodiments, the server 102 can reside in a data center and forma node in a cloud computing infrastructure. The server 102 can alsoprovide services on demand. A module hosting a client is capable ofmigrating from one server to another server seamlessly, without causingprogram faults or system breakdown. The server 102 on the cloud can bemanaged using a management system. Although FIG. 1 represents the server102 as a single server, the server 102 can include more than one server.

A client 106, which may be a target entity of the location informationanalytics platform, can include a desktop computer, a mobile computer, atablet computer, a cellular device, or any other computing deviceshaving a processor and memory. The server 102 and the one or more clientdevices 106 can communicate via the communication network 104.

In some embodiments, the client 106 can include user equipment of acellular network. The user equipment communicates with one or more radioaccess networks and with wired communication networks. The userequipment can be a cellular phone having phonetic communicationcapabilities. The user equipment can also be a smart phone providingservices such as word processing, web browsing, gaming, e-bookcapabilities, an operating system, and a full keyboard. The userequipment can also be a tablet computer providing network access andmost of the services provided by a smart phone. The user equipmentoperates using an operating system such as Symbian OS, iPhone OS, RIM'sBlackberry, Windows Mobile, Linux, HP WebOS, and Android. The screenmight be a touch screen that is used to location data to the mobiledevice, in which case the screen can be used instead of the fullkeyboard. The user equipment can also keep global positioningcoordinates, profile information, or other location information.

The client 106 also includes any platforms capable of computations.Non-limiting examples can include computers, netbooks, laptops, servers,and any equipment with computation capabilities. The client 106 isconfigured with one or more processors that process instructions and runsoftware that may be stored in memory. The processor also communicateswith the memory and interfaces to communicate with other devices. Theprocessor can be any applicable processor such as a system-on-a-chipthat combines a CPU, an application processor, and flash memory. Theclient 106 can also provide a variety of user interfaces such as akeyboard, a touch screen, a trackball, a touch pad, and/or a mouse. Theclient 106 may also include speakers and a display device in someembodiments.

The communication network 104 can include the Internet, a cellularnetwork, a telephone network, a computer network, a packet switchingnetwork, a line switching network, a local area network (LAN), a widearea network (WAN), a global area network, or any number of privatenetworks currently referred to as an Intranet, and/or any other networkor combination of networks that can accommodate data communication. Suchnetworks may be implemented with any number of hardware and softwarecomponents, transmission media and network protocols. Although FIG. 1represents the network 104 as a single network, the network 104 caninclude multiple interconnected networks listed above.

The server 102 can be configured to identify and classify specific areasof activity (AoA)—locations that a target entity visited in more thanone occasion—based on location information associated with the targetentity. The server 102 subsequently analyzes the AoAs collectively todetermine patterns of behavior and reference, and turn these patternsinto geographic, demographic, and/or behavioral profiles. FIG. 2illustrates an area of activity in accordance with some embodiments. InFIG. 2 , irregular and intermittent activities 202A-202C of a singletarget entity are normalized into an area of activity (AoA) 204.

The AoAs of a single target entity can be aggregated into a set of AoAs.FIG. 3 illustrates a set of AoAs in accordance with some embodiments.The size of the AoAs 204A-2040 can indicate the relative frequency ofactivities within that AoA over a predetermined period of time. Once theset of AoAs is determined, the server 102 can analyze general patternsof movements between the AoAs 204 and rank them by importance. FIG. 4illustrates patterns of movements between AoAs in accordance with someembodiments. For example, FIG. 4 shows, using a line 406, that themobile device frequently travels between Santa Monica 402 and LosAngeles 404. The frequency of the trip between two AoAs can beillustrated using a thickness of the line 406 bridging the two AoAs orby using a color-coding scheme. This analysis can enable the server 102to determine the zip code associated with the home residence, typicalcommuting start and end times, and diurnal behavior patterns to informthe predictive model.

In some embodiments, user activities around an AoA can be summarized ina profile. FIGS. 5A-5C illustrate a profile of user activities aroundAoAs in accordance with some embodiments. FIG. 5A illustrates a profileof user activities around Santa Monica, Calif., which the server 102identifies as “home”. The profile can include a map 502 indicating oneor more location data points 504 contributing to the AoA 506. Theprofile can also include a time-table 508, indicating time instances atwhich location data points 504 contributing to the AoA 506 appeared inthe vicinity of the AoA 506. The profile can further include a summary510 of the location data points 504 contributing to the AoA 506. FIG. 5Bsimilarly illustrate a profile of user activities around Los Angeles(Los Angeles), identified as a work place. FIG. 5C, illustrates useractivities associated with predetermined criteria. In this case, thepredetermined criteria are “user activities around Los Angeles (LosAngeles) from noon to 1 PM at locations matching nearby restaurants.”Such predetermined criteria allow the server 102 to infer user'sactivities at predetermined locations.

In addition to the locations, rank, and business categories of theseAoAs and their relative importance, the server 102 can provide a rankedbreakdown of all cities, regions, countries, metro areas, and DMAs in auser profile. More particularly, the server 102 can be configured todetermine whether a particular location is in a polygon (or a region ofinterest) corresponding to a particular city, region, country, metroarea, and/or a DMA, and provide the determined information as a part ofa user profile. For example, the server 102 is configured to uselocation information, such as a latitude/longitude pair, to identify forall of the “areas” (e.g., polygons) that encompass the locationassociated with the location information. This allows the server 102 toreceive attributes associated with each of those areas, including, forexample, the name of the area, the name of the regions that encompassthe area, and any metadata associated with the area or the region (e.g.average income, demographics). This information allows content providersto use location-based contextual information effectively to customizecontent events when a geo signal is absent.

FIG. 6 illustrates a process for generating a profile of a target entityin accordance with some embodiments. In step 1, the L1F module 112 canreceive, from service providers or mobile devices 106, locationinformation associated with the one or more mobile devices 106. In someembodiments, the location information can include one or more of (1)geospatial coordinates. (2) a timestamp, and/or (3) an identifier (ID)of a target entity. As an example, the single location data point can be[42.3583° N, 71.0603° W (Boston), 10:30 AM Jan. 10, 2012, John Doc'smobile phone], which indicates that John Doe's mobile phone was locatedin Boston at 10:30 AM on Jan. 10, 2012. The geospatial coordinates inthe location data point can take the form of a coordinate pair, forexample, [longitude, latitude], or other forms as would be used by atarget entity for indicating the location. Multiple location data pointsof this nature are used to create a profile. In some embodiments, theidentifier of the location information can be pre-hashed by the serviceprovider or the target entity so that the actual user of the targetentity remains anonymous to the server 102.

In some embodiments, the LIF module 112 can receive the locationinformation in bulk (e.g., an aggregated form). For example, the serviceprovider or the target entity can aggregate location information overtime and provide the aggregated location information in bulk (e.g.,substantially at the same time) to the LIF module 112. In otherembodiments, the LIF module 112 can receive the location information asit becomes available (e.g., in a streaming mode). For example, the LIFmodule 112 can receive, from the service provider or the one or more thetarget entities over a representational state transfer (REST) interface,the location information as it becomes available. In some embodiments,the LIF module 112 can receive the location information both in bulk andwhen it becomes available. For example, the L1F module 112 can receive,in bulk, the location information of the first mobile device and the LIFmodule 112 can receive the location information of the second mobiledevice in a streaming mode (e.g., as it becomes available).

Once the LIF module 112 receives the location information, the LIFmodule 112 can preprocess the location information. In particular, theLIF module 112 can be configured to quantize the spatial dimension ofthe received location information. For example, the LIF module 112 canbe configured to quantize the geospatial coordinate (e.g.,latitude/longitude pair) into a predetermined precision of coordinates.As another example, the LIF module 112 can be configured to quantize thegeospatial coordinate into a geohash representation.

In some embodiments, because the location information can be generatedby a variety of sources, for example, an on-device GPS system, a webbrowser, geotagged images, and business check-ins, some of the receivedlocation data points may not be sufficiently accurate. If the geospatialcoordinate in a location data point is not sufficiently accurate, theLIF module 112 can discard the location data point and the discardedlocation data point is not subject to further processing. This cleansingprocesses, e.g., processes that discover and remove location data pointsfrom further processing, can be performed across multiple useridentifiers, and can be done across multiple datasets for more accurateresults.

In some cases, some location data points are known to be bad orinaccurate. For example, if a data point corresponds to a center of anuclear plant, or to desert with no access path, then there is a highchance that the data point is bad or inaccurate. Therefore, in someembodiments, the LIF module 112 can maintain a blacklist of data pointcharacteristics that should be discarded. For example, the LIF module112 can identify, as black-listed, location data points that align tothe coordinates of known geographical entities (such as postcodes andcity centroids), or known cell-tower locations because they may be toocoarse for the application of interest.

In some embodiments, the LIF module 112 can discard the location datapoint if the confidence score, representing the accuracy of a locationdata point is below a predetermined threshold. The confidence score of alocation data point can be determined based on a variety of information.In some cases, if a particular location is over-represented at aparticular time instance (e.g., many IDs are associated with the samegeographical coordinate at the same time), then the confidence score ofthe location data point, indicating that a target entity is located atthat particular location at that particular time instance, can be low.

For example, if a town has 25,000 residents, and if the aggregatelocation information indicates that 22,000 target entities are locatedat a particular location at the same time, a location data pointindicating that a target entity is located at that particular locationis probably inaccurate. Therefore, such a location data point can beassociated with a low confidence score. As another example, if thenumber of location data points associated with a particular location ismore than 0.1% of the entire set of location data points, then it'shighly likely that location data points associated with the particularlocation is inaccurate. Therefore, such a location data point can beassociated with a low confidence score. In other words, if the number oflocation data points associated with a particular location is greaterthan a predetermined threshold, any location data point associated withthe particular location can be deemed inaccurate and be associated witha low confidence score.

In some embodiments, the predetermined threshold for discarding thelocation data point can be adapted to the characteristics of thelocation information data set. For example, the predetermined thresholdfor a data set associated with a small town can be different from thepredetermined threshold for a data set associated with Los Angeles,Calif.

In some embodiments, the LIF module 112 can apply a variety of filtersto further discard unwanted data points. For example, target entitiesthat do not have a sufficient volume of data can be eliminated fromfurther processing.

In some embodiments, the LIF module 112 can reduce the noise in thelocation data points. Because a target entity's movement is expected tobe smooth, any rapid movements centered around a particular location canbe considered as noise, which may stem from the non-idealcharacteristics of the location sensing device, such as a GPS jitter.Therefore, the LIF module 112 can model the temporal noise as a Gaussiandistribution and temporally average the location data points to removethe temporal jitter from the location data points. For instance, if atarget entity goes to the same Starbucks every day at 7 PM, and iflocation information of the target entity at 7 PM indicates that theuser is close to the same Starbucks, then the LIF module 112 cantemporally average the GPS coordinate so that the averaged GPScoordinate is more closely aligned with the same Starbucks. In someembodiments, the LIF module 112 can average location data points thatare within a predetermined time window from the location data point ofinterest. For example, the LIF module 112 can average location datapoints that are within a 5-second window from the location data point ofinterest. In other embodiments, the LIF module 112 can average locationdata points that belong to the same time instance in previous days ormonths. For example, in the above Starbucks example, the LIF module 112can average location data points associated with 7 PM for the last 5days to remove noise of today's location data point associated with 7PM.

In step 604, the LIF module 112 can optionally determine sessions andclusters from the pre-processed location information. A session is anabstraction used to remove redundancy from location data points. Thisabstraction can insulate the LIF module 112 from rapid resubmissions ofsimilar or identical location data points from the same target entity.For example, if the LIF module 112 receives a first location data pointand a second location data point from the same target entity within asecond of each other and from the same location, the LIF module 112 canfold those into a single session because the second data point does notprovide any interesting information. Another way to view the session isthat the session marks an event. The LIF module 112 is essentiallygrouping a stream of location data points into high-entropy events.

Based on this view of a session, a session can include a set of locationdata points from the same target entity (e.g., the same identifier)bounded in space and/or time. Therefore, the LIF module 112 can beconfigured to group any location data points that are bounded in spaceand/or time, and represent all grouped location data points using thetemporally earliest location data point in that group. For example, asession can include a group of location data points that are (1) within5 miles from the center of Los Angeles and/or that are (2) within a spanof 10 minutes. Therefore, the LIE module 112 can be configured to groupall data points that are (1) within 5 miles from the center of LosAngeles and/or (2) that are within a span of 10 minutes, and representall these data points using the earliest location data point within thatgroup of data points.

In some embodiments, the session can be represented using a plurality ofparameters. One of the plurality of parameters can represent a period oftime corresponding to the session; one of the plurality of parameterscan represent a geographical bound corresponding to the session.Therefore, as an example, a single session can include all location datapoints collected while a particular user's mobile phone was at aparticular location (e.g., between 100-120 Main Street) during aparticular time interval (e.g., from 10:00 AM to 10:15 AM on Jun. 12,2012). The L1F module 112 can, therefore, use the plurality ofparameters to group location data points into one or more sessions. Insome embodiments, the bounds for the space and/or time can be providedby an operator of the server 102. In other embodiments, the LIF module112 can automatically determine the bounds for the space and/or timeusing a clustering technique, such as K-means clustering.

In some embodiments, a session can include an event. A rapid change inthe entropy of a sequence of geolocation coordinates can be indicativeof a transition between two distinct events. Therefore, the LIF module112 can be configured to determine a rapid change in the entropy of asequence of geolocation coordinates.

In some embodiments, the LIF module 112 can use geographical polygons toidentify sessions from a sequence of geolocation coordinates. Forexample, a geographical polygon can be associated with a particularbuilding in Los Angeles. If a user is within the geographical polygon,then all temporally-bounded location data points associated with thegeographical polygon can be deemed to belong to the same session. TheLIF module 112 can use a hierarchical mechanism to quickly determinewhether a location data point is associated with a particulargeographical polygon. For example, the LIF module 112 can use thepolygon matching mechanism, as disclosed in “APPARATUS, SYSTEMS, ANDMETHODS FOR PROVIDING LOCATION INFORMATION,” supra.

During session processing, a location data point may be associated withonly one session. Conversely, one session might be associated with anynumber of location data points provided they satisfy the space and/ortime session parameters. For example, a session can include a singlelocation data point.

After session processing, location data points within a session canrepresent events or places visited by a particular ID.

In some embodiments, the LIF module 112 may not identify any sessionsfrom the location data points. For example, the LIF module 112 can skipthe session identification step. This scenario is identical to ascenario in which a session includes a single location data point.Therefore, the forthcoming discussion of using sessions to determineclusters and/or attributes can also be applicable to cases in which theLIF module 112 does not identify any sessions.

Once the LIF module 112 identifies one or more sessions from thetime-series of location data points, the LIF module 112 can determineone or more clusters based on the identified sessions. Clusters caninclude groupings of sessions which represent repeated behaviors overtime. Conceptually, any session with a new geo-spatial location cancorrespond to a new cluster, and later sessions can be added to one ofexisting clusters provided that these later sessions meet certaingeographic criteria. Therefore, the LIF module 112 is configured togroup one or more sessions into a single clusters by identifyingsessions that are geographically close to a center of a cluster. As anexample, the first time John Doe's mobile phone moves to a new location(e.g., 200 Main Street), location data associated with that new locationcan be grouped into a new session. If that same mobile phone laterreturns to that location (200 Main Street), data associated with thatsecond visit can be grouped into another session. Then, both sessionscan be grouped into a single cluster because the sessions are associatedwith the same location.

FIG. 7 illustrates a process for clustering two or more sessions into acluster in accordance with some embodiments. In step 702, the LIF module112 can optionally sort the sessions in the temporal order. For example,the LIF module 112 can order the sessions so that a session representedby the earlier location data point appears before a session representedby the later location data point. While this ordering step is notnecessary, this allows the LIF module 112 to order the clusterstemporally as well.

In step 704, the LIF module 112 can designate the first session as afirst cluster. In step 706, the LIF module 112 can analyze a subsequentsession, which is herein referred to as a candidate session. Inparticular, the LIF module 112 can determine the distance between thecenter of the candidate session and the center of the representativesession of the existing cluster. In step 708, the LIF module 112 candetermine whether the minimum of the distances computed in step 706 isless than a predetermined threshold. If so, in step 710, the LIF module112 can associate the candidate session with the cluster correspondingto the minimum distance. If not, in step 712, the LIF module 112 cancreate a new cluster for the candidate session and assign the candidatesession as the representative session of the new cluster.

In step 714, the LIF module 112 can repeat steps 706-712 until eachsession is associated with a cluster.

In some embodiments, in step 706, the LIF module 112 can be configuredto compute a distance between the center of the candidate session andthe center of all sessions in the existing cluster, instead of thedistance between the center of the candidate session and the center ofthe representative session of the existing cluster. The center of allsessions in the existing cluster can include a center of the centroidformed by the sessions in the existing cluster. When a session is addedto a cluster, the LIF module 112 can recomputed the center of allsessions in the cluster. In some embodiments, the clusters can be usedas AoAs, illustrated in FIGS. 2-5 .

FIG. 8 illustrates an example of how location data points are groupedinto sessions and sessions are grouped into clusters in accordance withsome embodiments. It will be appreciated that FIG. 8 presents asimplified case. In FIG. 8 , each session corresponds to a location andtime. In practice, location data may not be available at uniform timeintervals because users typically move from one location to another morerandomly and are not sending data at a constant rate. Sessions mayinclude many individual data points as Session 1 shows. Also, in FIG. 8, the location information is given in the form of an address, e.g., 200Main Street. However, the location information is typically in the formof a (longitude, latitude) geographical coordinate. Session and clusterprocessing is normally based on such geographical coordinates. Addresses(e.g., 200 Main Street) or other higher level location descriptors canbe added during the “annotation” processing step (step 606), which isdiscussed below.

Clusters are generally weighted based on the number of sessions thatthey contain. Thus, for a particular ID, there is normally a maincluster which represents the most visited geographic location, followedby any number of secondary clusters. In FIG. 8 , cluster 1 is the maincluster, and has the highest weight. Clusters 2 and 3 are the secondaryclusters.

Clusters can be constructed to compensate for small local movement(e.g., noise associated with location data points) while also accuratelypinpointing the specific location with which this movement isassociated. One way to achieve this is to divide geographic locationsinto overlapping tiles, e.g., of 50×50 meter squares. FIGS. 9A-9Gillustrate such a division for a small geographic area in accordancewith some embodiments. Each of the tiles shown in FIGS. 9A-9G is a 50×50meter square. The illustrated tiles are offset from one another by halfthe length of a tile, i.e., by 25 meters. With the overlappingarrangement illustrated in FIGS. 9A-9G, any point within the definedarea will fall within four tiles. For example, the point withcoordinates (10, 10) falls within the tiles illustrated in FIGS. 9A, 9E,9F, and 9G. As another example, the point with coordinates (49, 49)falls within the tiles illustrated in FIGS. 9A, 9B, 9C, and 9D. It willbe appreciated that the tiles can be extended beyond the rangeillustrated in FIG. 9 , maintaining the overlapping pattern, so as tocover a larger geographic area.

In some embodiments, the LIF module 112 can fine-tune the determinedclusters. In certain scenarios, a target entity may visit two near-bylocations for two different reasons. For example, on the way to work, atarget entity regularly visits 300 Greenwich Street, N.Y. for a cup ofcoffee, and on the way back home, the target entity regularly visits 301Greenwich Street, N.Y. for a food pickup. If the location data isinaccurate, then two entities would likely be merged into a singlecluster, and there would be no way to separate the merged entities.

The LIF module 112 can address this issue by clustering sessions basedon a variety of characteristics associated with sessions, not simplybased on location coordinates. For example, the LIF module 112 cancreate clusters based on the path or a temporal progression ofcoordinates or a time of visit. More particularly, in steps 706-712, theLIF module 112 can identify clusters by considering not only thephysical proximity, but also other types of relevant information, forexample, time information. This feature is useful in cases where twonearby locations have different semantic significance. For example, thisfeature is useful when two floors at the same building (hence thelatitude/longitude pair) are operated by two different companies.

To this end, in step 706, the LIF module 112 can quantize the time aswell as the geographical coordinate of a session (or, if the LIF module112 docs not identify sessions, the geographical coordinate of alocation data point). In particular, the LIF module 112 can beconfigured to identify common temporal patterns. For example, that asession or a location data point is associated with “end of a workday”or “Saturday morning.” Then, the LIF module 112 can be configured to addthe session or the location data point to either the last-visitedcluster or the last N visited clusters.

In some embodiments, the LIF module 112 can be configured to clusterevents using a maximum likelihood model. The LIF module 112 can beconfigured to characterize clusters based on an information gain alongany of information axes (for example, time, previous location). Then,when the LIF module 112 receives a new event to be added to one of theclusters, the LIF module 112 determines the likelihood that the newevent is associated with the clusters, and selects the cluster with themaximum likelihood.

More particularly, to characterize a cluster based on an informationgain, the LIF module 112 can be configured to receive all data pointscurrently within a cluster and quantize field values (e.g., attributes)associated with the data points. Then, the LIF module 112 can constructhistograms of these quantized fields and interpret them as statisticaldistributions. Subsequently, when the LIF module 112 receives a newevent, the LIF module 112 can determine a cluster to which the new eventshall be added by quantizing the fields in the new event, determiningprobabilities (e.g., Percent|cluster)) that the new event belongs to theclusters based on the histograms constructed previously, and selectingthe cluster with which the probability is the highest. In someembodiments, to avoid cases where the probability P(event|cluster) iszero, the LIF module 112 can add a noise floor to the histogramconstructed for each cluster.

In some embodiments, the LIF module 112 can be configured to merge twoor more clusters using a maximum-likelihood model. In some cases, theLIF module 112 can be configured to merge clusters based on a jointentropy between clusters, which can be measured based onKullback-Liebler (K.L) divergence. In other cases, the LIF module 112can merge clusters using any hierarchical clustering technique.

In step 606, the LIF module 112 can optionally annotate sessions and/orclusters using information from external data sources. In this step,sessions and clusters are enriched with outside data sources, a processknown as annotation. For example, information that describes thesurrounding business categories or demographics associated with alocation are appended to the corresponding cluster or session at thatlocation. Together, these annotations create a rich dataset that areused to enrich the profile of a target entity, and act as the foundationfor further annotations.

In some embodiments, the LIF module 112 can receive annotation data froman external database. In other embodiments, the LIF module 112 cananalyze the text on webpages to generate the annotation data.

In some embodiments, some internal optimizations can be made to reducethe number of queries the LIF module 112 makes to external data sources.These internal optimizations can include merging nearby sessions andreusing old queries by interpolating results.

In step 608, the LIF module 112 can provide the sessions, clustersand/or annotation data associated with a target entity to the LIA module114 so that the LIA module 114 can determine one or more profiles forthe target entity based on the sessions, clusters and/or annotationdata.

In some embodiments, the LIF module 112 may not identify any clustersfrom the sessions and/or location data points. For example, the LIFmodule 112 can skip the cluster identification step. This scenario isidentical to a scenario in which a cluster includes a single session ora single location data point. Therefore, the forthcoming discussion ofusing clusters to determine attributes can also be applicable to casesin which the LIF module 112 does not identify any clusters.

The LIA module 114 is configured to analyze the location data points,sessions, clusters and/or annotation data over a period of time to builda general description of the target entity and provide the generaldescription in the form of a profile. The profile can be a JavaScriptObject Notation (JSON) hash. The profile can include (1) one or moreattribute values describing the target entity and (2) one or moreconfidence scores associated with the one or more attribute values. Insome embodiments, the attribute values can include (1) areas ofactivity, categorized by business type and summarized by country, metroarea, and DMA, (2) demographic information, including household incomewhere available, and/or (3) behavioral traits and classifications. Theprofile can be specifically designed to enhance the interaction betweena service provider and individual users. The profile can help serviceproviders to serve correct local news and more relevant information, tocustomize information content by location, and to ensure that only themost contextually relevant information is served to users at the righttime.

In some embodiments, the geographic attribute of a profile can providean overview of locations that the target entity is most active. Thelocations can be represented at different physical scales, for example,at a hyper-local, a regional, and/or a national scale. The profile canalso include specific, hyper-local places associated with the useractivity, including, for example, centroids of the hyper-local places,associated postcodes, the type of hyper-local places, and/or thecommercial density of the area. FIG. 10 illustrates the geographicattribute of a profile in accordance with some embodiments. Thegeographic attribute of a profile includes an “Area of Activity (AoA)”entry, which includes an identifier of the AoA, the center of the AoA,the commercial density in the AoA (which measures a portion of the AoAcorresponding to a commercial area), a postcode associated with the AoA,the ranks of the AoA (e.g., frequency or importance associated with theAoA), and the type of the hyper-local place (e.g., away, indicating thatthe AoA is not a primary area of activity.)

In some embodiments, the profile can also include an attributeindicating a home location for the target entity. The LIA module 114 canbe configured to determine the home location (also known as a homeattribute of the profile) by analyzing the movement patterns of thetarget entity between AoAs, whether a particular location (e.g., the AOAcorresponding to the particular location) is known to be (or is likelyto be) a residence, including, for example, a house, an apartment and acondo, a commercial/residential density around the location, a frequencyof the particular location (e.g., the AOA corresponding to theparticular location) relative to other AoAs, and/or the timestamps oflocation data points associated with the movement patterns. In someembodiments, the home location can be represented as a postal code. Inother embodiments, the home location can be represented as a geospatialcoordinate, such as a GPS coordinate or a latitude/longitude coordinatepair.

FIG. 11 illustrates a home location attribute of a profile of a targetentity in accordance with some embodiments. The home location attributecan indicate the center of the centroid (or an AoA) corresponding to thehome location, the commercial density in the AoA, and a postcode. Insome embodiments, the postcode in the home location attribute can beassociated with the home location itself. In other embodiments, thepostcode in the home location attribute can be associated with the AoAwithin which the home is located. Such embodiments can improve theprivacy of users. In some embodiments, the commercial density can berepresented as a percentage. For example, the percentage of theresidential area within the centroid of the home location can be 81.1%,as indicated in FIG. 11 , and the percentage of the businesses withinthe centroid of the home location can be 4.6%. The commercial densitycan be useful in determining the urban/suburban/rural context of auser's residence.

In some embodiments, the LIA module 114 can be configured to provide alist of location entities in which the user has been active. Thelocation entities can include countries, regions, and/or localities(towns). The LIA module 114 can provide the list of such locationentities in the order of relative significance. In some cases, the LIAmodule 114 can determine the significance associated with the locationentities based on the amount of time spent at a particular locationentity. For example, when a user stays at home 90% of the time, home maybe an important location entity. In some cases, the LIA module 114 candetermine the significance associated with the location entities basedon how informative the annotations associated with the particularlocation entity are. Also, in some cases, the LIA module 114 candetermine the significance associated with the location entities basedon how important the behavior associated with the particular locationentity is. For example, when a user stays home 90% of the time, but theuser also spends 10 minutes at a school in the morning and afternoon,this location entity can be important because it may tell us that theuser is likely a parent.

FIG. 12 illustrates a list of location entities provided in a profile ofa target entity in accordance with some embodiments. Each locationentity can be associated with a particular geographical scale. Forexample, FIG. 12 shows three location entities, the first entityassociated with “countries”, the second entity associated with“regions”, and the third entity associated with “localities.” Eachlocation entity can be associated with a unique identifier (referred toas “factual id” in FIG. 12 ), and can be associated with a location datapoint, such as a latitude/longitude coordinate pair. Also, the one ormore of the entries in the location entity can be associated with aconfidence score.

In some embodiments, when the target entity is associated with alocation within the United States, the profile can also includeadditional geographic summaries. The additional geographic summaries caninclude Nielsen's Direct Marketing Area (DMA) and/or Metro (formerly MSAas defined by the United States census) in which the user is active.FIGS. 13A-13B illustrate the DMA attribute and the Metro attribute in aprofile of a target entity in accordance with some embodiments.

In some embodiments, the L1A module 114 can determine demographicattributes for the target entity's profile. In particular, the LIAmodule 114 can determine the demographic attributes based on the user'shome location, aggregated at the block group level by the US censusdata, and/or based on the user's activities. FIG. 14 illustratesdemographic attributes of a profile of a target entity in accordancewith some embodiments. The demographic attributes can include an incomelevel, a gender, age, a household type, and race. Also, one or more ofthe demographic attributes can be associated with a confidence score.

In some embodiments, the LIA module 114 can determine behavioralattributes for the target entity's profile. The behavioral attributescan categorize target entities into one or more categories, which can beused in online advertising. Target devices are categorized only when theconfidence score is sufficiently high to qualify their presence in thesecategories. FIG. 15 illustrates behavioral attributes of a profile of atarget entity in accordance with some embodiments. Behavioral attributescan include one or more predetermined categories and a confidence scoreindicating the likelihood that the target entity is associated with theone or more predetermine categories. The predetermined categories caninclude: whether the target entity is owned by a business traveler,whether the target entity is owned by a leisure traveler, whether thetarget entity is owned by a frequent traveler, whether the target entityis owned by a health care provider, whether the target entity is ownedby a college student, whether the target entity is owned by a personinterested in buying a car, whether the target entity is owned by amoviegoer, whether the target entity is owned by a vacationer, whetherthe target entity is owned by a live sports fan, and/or whether thetarget entity is owned by an affluent customer. Standard categories canbe applied across the dataset to find groups of IDs that sharetendencies or patterns.

In some embodiments, the LIA module 114 can determine the likelihoodthat a target entity is associated with a particular category, alsoreferred to as a category confidence score, based on sessions, clusters,and/or annotation data associated with the target entity. For example,annotated clusters may indicate that an ID (a target entity) is biasedto operate in more expensive demographic regions, and annotated sessionsmay indicate that an ID has traveled from one location to another.

In some embodiments, the attributes in the profile can be updatedregularly so that the profile does not become stale (or out-of-date).For example, the LIA module 114 can be configured to recomputeattributes of a profile periodically. Also, the LIA module 114 can beconfigured to reduce a confidence score value for an attribute as theattribute ages (e.g., as a function of the time instance at which theattribute was generated or computed).

In some embodiments, the LIA module 114 can use a cross-validationmechanism to determine whether a target entity is associated with aparticular attribute or category. At a high level, the LIA module 114 isconfigured to extract features that represent what the target entity isdoing at any given moment. These features are generated from nearbyplaces (if there are any), time-localized events (such as concerts), andlandmarks. For example, when a target entity is going to the LA Dodgersstadium, and if, at that time instance, the Dodgers stadium is hosting arock concert, then the target entity can be preferentially correlatedwith rock music. As another example, the LIA module 114 can determinethe home location by determining the location to which a target entityreturns or stays the most at night time, such as after 8 PM.

To this end, the LIA module 114 can, for example, determine the businesscategory, each word in the business name, and high-level categoryinformation about events as individual features. Subsequently, the LIAmodule 114 weighs these features, for example, equally, and use the norm(e.g., an LI-norm) of the resulting vector to perform thecross-validation.

One of the challenges in categorizing the behavior of a target entity ismaking sure that the LIA module 114 does not over-fit the model. Forexample, suppose the LIA module 114 finds that a target entity is nextto Starbucks on Friday, July 17, at 15:38 pm only. While the physicallocation of the target entity corresponding to the target entity at thattime instance is a remarkably strong signal, spending effort narrowingdown the time slice of that observation isn't particularly meaningful.As a result, the LIA module 114 can consider both the amount of effortit takes to describe the observation and the strength of the observationresults when the LIA module 114 draws conclusions about a target entity.So, for example, if the LIA module 114 observes that the target entityis next to a Starbucks at 4 pm every Friday for four Fridays in a row,that is a more valuable insight.

In some embodiments, the LIA module 114 is configured to determinebehavioral categories of target entities based on a number of times aparticular space-time location data point (or a cluster) appears in thetime-series of location data points. To this end, the L1A module 114 isconfigured to (1) project time component of the location data point orthe cluster into a cyclic space (e.g., a 24-hour time span, ignoring thedate), (2) determine a number of times a particular [space,projected-time] representation of the location data point or the clusterappears in the time-series of location data points, and (3) determinethe K-highest number of the [space, projected-time] in the time-seriesof location data points. More particularly, the L1A module 114 isconfigured to (1) represent each of the location data point into a pairof [space, projected-time], (2) construct a frequency table of suchpairs and, optionally, sort the entries in the frequency table by adescending frequency, and (3) identify pairs from this frequency tablewhose frequency is greater than a predetermined threshold, such as two.

Other examples of useful properties to note as a result ofcategorization processing are:

-   -   A particular person is a habitual Starbucks customer. Although        this person travels a lot throughout the country, regardless of        his/her current location, he/she frequently visits a Starbucks        between 8:30-9:00 am.    -   A particular person consistently shops at high end, expensive        stores, and never visits low end, inexpensive stores.    -   A particular person likes hamburgers and is likely to visit any        restaurant convenient to his location that serves hamburgers.    -   A particular person is a fan of the New York Yankees; this        person attends Yankee games, both home and away, and also visits        stores that sell sports memorabilia.

Other types of categorization are possible, and will in general bedependent on the quality of the location data and the types ofannotations that have been annotated to the location information.

In some cases, the LIA module 114 can perform the profile computation inbulk. For example, a service provider may have months or years ofgeospatial information for many users and devices, and these can beprocessed by the LIA module 114 in bulk to provide an analysis for thetime span covered. In other cases, the LIA module 114 can perform theprofile computation as new location data points become available. Forexample, a service provider may not have detailed logs of location datapoints over a long period of time, but may instead have access to anephemeral stream of location data points in real-time or a rolling logof the previous day's location data points. In these cases, the serviceprovider can utilize the LIF module 112 and the L1A module 114 byintermittently posting new location data to the LIF module 112 as itbecomes available.

In some embodiments, the location data points can be processed in abatch or in a real-time mode. In a real-time mode, the LIA module 114can collate the new information with past information to build a newprofile. This collation can happen by any number of means, for example,key/value lookup or table joining. In some cases, the LIA module 114 canperform a preliminary processing of new information to determine apriority of profile updates. For example, when the LIA module 114determines that a mobile device is currently located within a boundingbox of interest near a sporting event, the LIA module 114 can flag thata user corresponding to the mobile device is a high priority target forreceiving a profile update.

Once the LIA module 114 completes profile computations, the LIA module114 can provide the computed profiles to a service provider. In someembodiments, the service provider can receive the computed profiles inbulk. In other embodiments, the service provider can query the LIFmodule 112 and/or the LIA module 114 incrementally to get the computedprofiles as needed, or can receive bulk profile updates at scheduledintervals.

In some embodiments, the profiles generated by the LIA module 114 can beaccessible only by the application that provided the locationinformation to the LIA module 114. Also, the target entities received bythe LIF module 112 and/or the LIA module 114 can be hashed or encryptedprior to the receipt by the LIF module 112 and/or the LIA module 114,thereby providing anonymity of users. These features can allow the LIFmodule 112 and/or the LIA module 114 to respect privacy of targetentities that provided the location data points to the LIF module 112and/or the LIA module 114.

In some embodiments, the computed profile can be provided in a tabularform. FIG. 16 illustrates a profile of a target entity in a tabular formin accordance with some embodiments.

In some embodiments, the LIA module 114 can be configured to learn apredictive model, based on the computed profile, that can predict abehavior of the target entity associated with the computed profile. Insome cases, the predictive model can be a non-parametric model.

FIG. 17A illustrates a process of learning a predictive model inaccordance with some embodiments. In step 1702, the LIA module 114 isconfigured to represent a target entity into a characteristic vector.The characteristic vector can be indicative of a variety ofcharacteristics associated with a target entity. For example, thecharacteristic vector can include attributes in the computed profile oftarget entities. As another example, the characteristic vector caninclude an clement that is indicative of whether the target entity isoperated by a male or female. When the target entity is operated by amale, an clement in the characteristic vector can have a value of “1”;when the target entity is operated by a female, an element in thecharacteristic vector can have a value of “0”; and when the targetentity is operated by an unknown gender, an element in thecharacteristic vector can have a value of “0.5”.

In step 1704, the LIA module 114 can optionally associate thecharacteristic vector of the target entity with the location data pointsof the target entity to form a feature vector. For example, thecharacteristic vector of the target entity can be concatenated with thelocation data points of the target entity to form the feature vector. Insome cases, the location data points can be represented as variousspatial and temporal resolutions. For example, the spatial coordinatesof the location data points can be represented using geohashes having apredetermined precision (e.g., anywhere between 20 to 40 bits ofprecision), and the temporal information associated with the spatialcoordinates can be represented at various temporal resolutions (e.g.,anywhere between 15 minutes to 6 hours).

In step 1706, the LIA module 114 can cluster ail feature vectorscorresponding to all target entities in a dataset, and average thefeature vectors in each cluster to form an average vector for eachcluster. Along with the average vector, the LIA module 114 can alsomaintain the number of target entities corresponding to each cluster.The average vector of a cluster and, optionally, the number of targetentities corresponding to the cluster can form a non-parametricpredictive model for behavioral characteristics.

In some embodiments, step 1704 is skipped and the characteristic vectoris used as the feature vector for step 1706.

In some embodiments, the LIA module 114 can use the predictive model topredict a behavior of the target entity associated with the computedprofile. FIG. 17B illustrates a process of using the predictive model topredict a behavior of the target entity associated with the computedprofile. In step 1752, the LIA module 114 is configured to determine afeature vector of the target entity. The feature vector can be formattedin accordance with the format vector used to train the predictive modelin step 1704, In step 1754, the LIA module 114 can retrieve thepredictive model, and in step 1756, the LIA module 114 is configured todetermine an association between the feature vector of the target entityand clusters in the predictive model. The association can be determinedby finding a set of weights to be applied to the average vector ofclusters to represent the feature vector as a weighted average ofaverage vector of clusters. The set of weights can identify the amountof shared information between the feature vector and the predictivemodel.

The terms “a” or “an,” as used herein throughout the presentapplication, can be defined as one or more than one. Also, the use ofintroductory phrases such as “at least one” and “one or more” should notbe construed to imply that the introduction of another element by theindefinite articles “a” or “an” limits the corresponding element to onlyone such element. The same holds true for the use of definite articles.

It is to be understood that the disclosed subject matter is not limitedin its application to the details of construction and to thearrangements of the components set forth in the following description orillustrated in the drawings. The disclosed subject matter is capable ofother embodiments and of being practiced and carried out in variousways. Also, it is to be understood that the phraseology and terminologyemployed herein are for the purpose of description and should not beregarded as limiting.

As such, those skilled in the art will appreciate that the conception,upon which this disclosure is based, may readily be utilized as a basisfor the designing of other structures, methods, and systems for carryingout the several purposes of the disclosed subject matter. It isimportant, therefore, that the claims be regarded as including suchequivalent constructions insofar as they do not depart from the spiritand scope of the disclosed subject matter.

Although the disclosed subject matter has been described and illustratedin the foregoing exemplary embodiments, it is understood that thepresent disclosure has been made only by way of example, and thatnumerous changes in the details of implementation of the disclosedsubject matter may be made without departing from the spirit and scopeof the disclosed subject matter.

We claim:
 1. An apparatus comprising: a processor configured to acquirecomputer readable instructions stored in one or more memory devices andexecute the instructions to: process a time-series of location datapoints for a target entity, wherein the time-series of location datapoints are received from a computing device associated with the targetentity; determine an accuracy of the time-series of the location datapoints, and discard, based on the determined accuracy, one or more ofthe location data points in the time-series of the location data points;determine one or more sessions from the time-series of location datapoints by grouping one or more of the time-series of location datapoints; determine one or more attributes associated with the targetentity based on one or more of, the time-series of location data points,or the one or more sessions; and generate a profile of the target entitybased on the one or more attributes associated with the target entity.2. The apparatus of claim 1, wherein determining the accuracy of thetime-series of the location data points based on a time-series of thelocation data points associated with other target entities.
 3. Theapparatus of claim 1, wherein determining the accuracy of thetime-series of the location data points at a particular time instancebased on location information associated with other target entities atthe particular time instance.
 4. The apparatus of claim 1, wherein theprocessor is further configured to execute the instructions to:determine one or more clusters of sessions based on a physical proximitybetween the one or more sessions; and wherein determining the one ormore attributes associated with the target entity is based on the one ormore sessions and the one or more clusters of sessions.
 5. A methodcomprising: processing a time-series of location data points for atarget entity received from a second computing device associated withthe target entity; determining an accuracy of the time-series of thelocation data points, and discard, based on the determined accuracy, oneor more of the location data points in the time-series of the locationdata points; determining one or more sessions from the time-series ofthe location data points by grouping one or more of the time-series ofthe location data points; determining one or more attributes associatedwith the target entity based on one or more of, the time-series of thelocation data points, and the one or more sessions; and generating aprofile of the target entity based on the one or more attributesassociated with the target entity.
 6. The method of claim 5, furthercomprising: determining one or more clusters of sessions based on aphysical proximity between the one or more sessions; and wherein the oneor more attributes associated with the target entity are determinedbased on the one or more sessions and the one or more clusters ofsessions.
 7. The method of claim 6, further comprising: annotating, theone or more clusters of sessions with annotation information associatedwith a geographical location of the one or more clusters of sessions;and wherein the one or more attributes associated with the target entityare determined based on the annotation information.
 8. The method ofclaim 5, further comprising: ranking patterns of movement of the targetentity; and generating at least one prediction of a future estimatedlocation of the target entity based on a ranking of the patterns ofmovement of the target entity.
 9. The method of claim 5, wherein theprofile is a behavior profile describing behavioral characteristics ofthe target entity.
 10. The method of claim 5, wherein the profile is ademographic profile describing a least one of a marketing segment of thetarget entity, the marketing segment comprising one or more of agebands, social class bands, or gender bands.
 11. The method of claim 5,wherein the profile is a geographic profile describing locations of aconnected series of events associated or locations visited by the targetentity.
 12. The method of claim 5, wherein the profile is an aggregateprofile comprising one or more of: behavioral attributes; marketingattributes; or geographic attributes.
 13. The method of claim 5, furthercomprising determining a home location attribute based on, at least inpart, a likelihood that a particular location is associated with aresidence.
 14. The method of claim 5, further comprising determining ahome location attribute based on, at least in part, timestamps of thelocation data points associated with the target entity.
 15. Anon-transitory computer readable medium having executable instructionsexecutable to cause a data processing apparatus to: process atime-series of location data points for a target entity, wherein thetime-series of location data points are received from a computing deviceassociated with the target entity; determine an accuracy of thetime-series of the location data points, and discard, based on thedetermined accuracy, one or more of the location data points in thetime-series of the location data points; determine one or more sessionsfrom the time-series of location data points by grouping one or more ofthe time-series of the location data points; determine one or moreattributes associated with the target entity based on one or more of,the time-series of the location data points, and the one or moresessions; and generate a profile of the target entity based on the oneor more attributes associated with the target entity.
 16. Thenon-transitory computer readable medium of claim 15, wherein theexecutable instructions are further executable to cause the dataprocessing apparatus to: determine one or more clusters of sessionsbased on a physical proximity between the one or more sessions; andwherein determining the one or more attributes associated with thetarget entity is based on the one or more sessions and the one or moreclusters of sessions.
 17. The non-transitory computer readable medium ofclaim 15, wherein the executable instructions are further executable tocause the data processing apparatus to determine a home locationattribute based on, at least in part, a likelihood that a particularlocation is associated with a residence.
 18. The non-transitory computerreadable medium of claim 15, wherein the executable instructions arefurther executable to cause the data processing apparatus to determine apredictive model based on the one or more attributes, wherein thepredictive model is configured to predict a behavior of the targetentity in a future.
 19. The non-transitory computer readable medium ofclaim 15, wherein the profile comprises at least one of: behavioralcharacteristics of the target entity; information describing a least oneof a marketing segment of the target entity, the marketing segmentcomprising one or more of age bands, social class bands, or genderbands; or information describing locations of a connected series ofevents associated or locations visited by the target entity.
 20. Thenon-transitory computer readable medium of claim 15, wherein the profileis an aggregate profile comprising one or more of: behavioralattributes; marketing attributes; or geographic attributes.